Remark 2 (Incorporating node features) 
GNNs are well suited to leverage the structure of a given graph to process values defined on its nodes, often referred to as node features or graph signals [1]}, [2]}.
However, in our proposed implementation [cf. (REF ) and (REF )], there are no node features involved.
In the presence of node features, our proposed framework can be easily enriched to encompass this information.
More precisely, collecting \(F^{\prime }\)  node features in the matrix \({\mathbf {Q}}\in {\mathbb {R}}^{N \times F^{\prime }}\)  where \(q_{if}\)  stores the values of feature \(f\)  at node \(i\) , we may consider functions of the type \(\Psi ({\mathbf {H}}, {\mathbf {Q}}; \theta )\)  in (REF ).
In particular, we still propose the GNN in (REF ) replacing \(\mathbf {1}\)  in () by \({\mathbf {Q}}\)  and resizing the parameter matrices accordingly. The theory discussed in this section still holds, where the modified notion of permutation equivariance boils down to \(\Phi (\Pi {\mathbf {H}}\Pi ^\top , \Pi {\mathbf {Q}}; \Theta ) = \Pi \Phi ( {\mathbf {H}}, {\mathbf {Q}}; \Theta )\) .
Examples of node features in the context of power allocation include relative node weights in the determination of a weighted sum-rate utility, queue lengths, traffic rates, and topological features of the underlying graph; see Section REF  for an implementation of this enriched setup.

